103 OptoSense: Towards Ubiquitous Self-Powered Ambient Light Sensing Surfaces DINGTIAN ZHANG, Georgia Institute of Technology JUNG WOOK PARK, Georgia Institute of Technology YANG ZHANG, Carnegie Mellon University YUHUI ZHAO, Georgia Institute of Technology YIYANG WANG, Georgia Institute of Technology YUNZHI LI, Georgia Institute of Technology TANVI BHAGWAT, Georgia Institute of Technology WEN-FANG CHOU, Georgia Institute of Technology XIAOJIA JIA, Georgia Institute of Technology BERNARD KIPPELEN, Georgia Institute of Technology CANEK FUENTES-HERNANDEZ, Georgia Institute of Technology THAD STARNER, Georgia Institute of Technology GREGORY D ABOWD, Georgia Institute of Technology Ubiquitous computing requires robust and sustainable sensing techniques to detect users for explicit and implicit inputs. Existing solutions with cameras can be privacy-invasive. Battery-powered sensors require user maintenance, preventing practical ubiquitous sensor deployment. We present OptoSense, a general-purpose self-powered sensing system which senses ambient light at the surface level of everyday objects as a high-fdelity signal to infer user activities and interactions. To situate the novelty of OptoSense among prior work and highlight the generalizability of the approach, we propose a design framework of ambient light sensing surfaces, enabling implicit activity sensing and explicit interactions in a wide range of use cases with varying sensing dimensions (0D, 1D, 2D), felds of view (wide, narrow), and perspectives (egocentric, allocentric). OptoSense supports this framework through example applications ranging from object use and indoor trafc detection, to liquid sensing and multitouch input. Additionally, the system can achieve high detection accuracy while being self-powered by ambient light. On-going improvements that replace Optosense’s silicon-based sensors with organic semiconductors (OSCs) enable devices that are ultra-thin, fexible, and cost efective to scale. CCS Concepts: · Human-centered computing → Ubiquitous and mobile computing; Interaction devices. Authors’ addresses: Dingtian Zhang, Georgia Institute of Technology, Atlanta, Georgia, dingtianzhang@gatech.edu; Jung Wook Park, Georgia Institute of Technology, Atlanta, Georgia, jwpark@gatech.edu; Yang Zhang, Carnegie Mellon University, Pittsburgh, Pennsylvania, yang. zhang@cs.cmu.edu; Yuhui Zhao, Georgia Institute of Technology, Atlanta, Georgia, yzhao343@gatech.edu; Yiyang Wang, Georgia Institute of Technology, Atlanta, Georgia, diana.wang@gatech.edu; Yunzhi Li, Georgia Institute of Technology, Atlanta, Georgia, yunzhi@gatech.edu; Tanvi Bhagwat, Georgia Institute of Technology, Atlanta, Georgia, tbhagwat6@gatech.edu; Wen-Fang Chou, Georgia Institute of Technology, Atlanta, Georgia, wfchou@gatech.edu; Xiaojia Jia, Georgia Institute of Technology, Atlanta, Georgia, xjia30@gatech.edu; Bernard Kippelen, Georgia Institute of Technology, Atlanta, Georgia, kippelen@gatech.edu; Canek Fuentes-Hernandez, Georgia Institute of Technology, Atlanta, Georgia, canek@ece.gatech.edu; Thad Starner, Georgia Institute of Technology, Atlanta, Georgia, thad@gatech.edu; Gregory D Abowd, Georgia Institute of Technology, Atlanta, Georgia, abowd@gatech.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. © 2020 Association for Computing Machinery. 2474-9567/2020/9-ART103 $15.00 https://doi.org/10.1145/3411826 Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 4, No. 3, Article 103. Publication date: September 2020.